最大限度地减少在线乘车和送货服务的延迟
Minimizing Latency in Online Ride and Delivery Services
摘要：
受在线乘车和送货服务的流行的启发，我们研究了经典的多车辆最小延迟问题的自然变体，其目标是路由位于仓库的一组车辆，以服务于位于度量空间上的请求，从而最大限度地减少总延迟。在本文中，我们考虑了带有源-目的地对的点对点请求，以及限制每个请求何时可以被服务的释放时间约束。点对点的请求和发布时间约束为出租车乘坐和交付建模。对于所有考虑的变量，我们展示了基于线性规划框架的常数因子近似算法。据我们所知，这些是最小延迟问题的上述变体的第一组结果。此外，我们提供了一个实证研究的基础上，我们的理论算法对一个真实的数据集的出租车车程。
英文摘要：
Motivated by the popularity of online ride and delivery services, we study natural variants of classical multi-vehicle minimum latency problems where the objective is to route a set of vehicles located at depots to serve request located on a metric space so as to minimize the total latency. In this paper, we consider point-to-point requests that come with source-destination pairs and release-time constraints that restrict when each request can be served. The point-to-point requests and release-time constraints model taxi rides and deliveries. For all the variants considered, we show constant-factor approximation algorithms based on a linear programming framework. To the best of our knowledge, these are the first set of results for the aforementioned variants of the minimum latency problems. Furthermore, we provide an empirical study of heuristics based on our theoretical algorithms on a real data set of taxi rides.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
